KEGG: Kyoto Encyclopedia of Genes and Genomes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from genomic and molecular-level information. It is a computer representation of the biological system, consisting of molecular building blocks of genes and proteins (genomic information) and chemical substances (chemical information) that are integrated with the knowledge on molecular wiring diagrams of interaction, reaction and relation networks (systems information). It also contains disease and drug information (health information) as perturbations to the biological system.

References in zbMATH (referenced in 265 articles )

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  1. Afzal, Arfan Raheen; Yang, Jing; Lu, Xuewen: Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters (2021)
  2. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  3. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  4. He, Yinqiu; Xu, Gongjun; Wu, Chong; Pan, Wei: Asymptotically independent U-statistics in high-dimensional testing (2021)
  5. Li, Yunfan; Datta, Jyotishka; Craig, Bruce A.; Bhadra, Anindya: Joint mean-covariance estimation via the horseshoe (2021)
  6. Łukasz Szeremeta; Dominik Tomaszuk: Generating molecular entities as structured data (2021) not zbMATH
  7. Otero, Marcelo; Sarno, Silvina N.; Acebedo, Sofía L.; Ramírez, Javier A.: Tracing molecular properties throughout evolution: a chemoinformatic approach (2021)
  8. Zhu, Yuanyuan; Hu, Bin; Chen, Lei; Dai, Qi: iMPTCE-Hnetwork: a multilabel classifier for identifying metabolic pathway types of chemicals and enzymes with a heterogeneous network (2021)
  9. Ajmal, Hamda B.; Madden, Michael G.: Inferring dynamic gene regulatory networks with low-order conditional independencies -- an evaluation of the method (2020)
  10. Granata, Ilaria; Guarracino, Mario Rosario; Maddalena, Lucia; Manipur, Ichcha: Network distances for weighted digraphs (2020)
  11. Li, Chunlin; Shen, Xiaotong; Pan, Wei: Likelihood ratio tests for a large directed acyclic graph (2020)
  12. Peeters, Carel F. W.; van de Wiel, Mark A.; van Wieringen, Wessel N.: The spectral condition number plot for regularization parameter evaluation (2020)
  13. Peng, Si; Shen, Xiaotong; Pan, Wei: Reconstruction of a directed acyclic graph with intervention (2020)
  14. Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi: PolyLinkR: A linkage-sensitive gene set enrichment R package (2020) arXiv
  15. Sanchez, Martin Jose Angel; Petre, Ion: Network controllability analysis of three multiple-myeloma patient genetic mutation datasets (2020)
  16. Su, Yansen; Zhu, Huole; Zhang, Lei; Zhang, Xingyi: Identifying disease modules based on connectivity and semantic similarities (2020)
  17. van Wieringen, Wessel N.; Stam, Koen A.; Peeters, Carel F. W.; van de Wiel, Mark A.: Updating of the Gaussian graphical model through targeted penalized estimation (2020)
  18. Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
  19. Wu, Chong; Xu, Gongjun; Shen, Xiaotong; Pan, Wei: A regularization-based adaptive test for high-dimensional GLMs (2020)
  20. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)

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